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Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly

机译：自动化设备作为社区居民老年人跌倒风险评估的筛查工具的诊断测试准确性

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We aimed to determine the accuracy and failure of OAK device, an automated screening, for the assessment of fall risk in a prospective cohort of healthy adults aged over 65 years. The algorithm for fall risk assessment of the centers for disease control and prevention (CDC) was used as reference standard. Of the 183 individuals recruited, the CDC algorithm classified 80 as being at moderate/high risk and 103 at low risk of falling. OAK device failure incidence was 4.9% (confidence interval [CI] upper limit 7.7%), below the preset threshold for futility-early termination of the study (i.e., not above 15%). The OAK device showed a sensitivity of 84% and a specificity of 67% (receiver operating characteristic [ROC] area 82%; 95% confidence interval [CI] 76–88%), not reaching the preplanned target sensitivity (not lower than 85%). Diagnostic accuracy was not far from the sensitivity levels similar to those obtained with other fall risk assessment. However, some limitations can be considered.ClinicalTrials.gov identifier: .
机译：我们旨在确定OAK设备（自动筛查）的准确性和故障率，以评估65岁以上健康成年人的预期队列中的跌倒风险。疾病控制和预防中心（CDC）的跌倒风险评估算法用作参考标准。在招募的183个人中，CDC算法将80个分类为中度/高风险，将103个分类为低落风险。 OAK设备故障发生率为4.9％（置信区间[CI]上限为7.7％），低于无用功终止研究的预设阈值（即不超过15％）。 OAK设备显示出84％的灵敏度和67％的特异性（接收器工作特征[ROC]区域82％； 95％置信区间[CI] 76-88％），未达到预定的目标灵敏度（不低于85 ％）。诊断准确性与其他跌倒风险评估所获得的敏感性水平相差不远。但是，可以考虑一些限制。ClinicalTrials.gov标识符：。

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